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Risk Fraud & Compliance

Using ID verification to prevent fraud, waste & abuse in government unemployment agencies

Serena Dibra  Associate Product Marketing Manager / Risk & Fraud / 黑料大湿Posts

Andrew Pellington  Senior Director of Product Marketing / Risk & Fraud / 黑料大湿Posts

· 5 minute read

Serena Dibra  Associate Product Marketing Manager / Risk & Fraud / 黑料大湿Posts

Andrew Pellington  Senior Director of Product Marketing / Risk & Fraud / 黑料大湿Posts

· 5 minute read

Government agencies can utilize identity verification in order to better prevent fraud and protect public funds from illicit actors who increasingly use more sophisticated tactics

State and local government agencies are under immense pressure to deliver critical public benefits, often with constrained resources; and preventing, detecting, and ultimately (and where necessary) investigating potential fraud can be a challenging process.

It鈥檚 critical for government agencies to get the process right, however, especially in an environment in which the efficient and effective use of public funds is scrutinized by a variety of parties, including the general public.

Government unemployment agencies, for example, continue to deal with what is seemingly a trifecta of reinforcing complications, including that i) many agencies lost critical talent during the pandemic; ii) recruiting new talent remains a top concern; and iii) modernizing systems is replete with challenges. Sprinkle in the fact that bad actors are employing ever more sophisticated methods to commit fraud and you have a perfect storm.

All of this makes the stakes of getting identity (ID) verification and, more broadly, fraud prevention correct very high. The recent volume of unemployment insurance (UI) investigations has reached over which has stressed many agencies in a variety of ways. Further, the U.S. Government Accountability Office (GAO) $19 billion in incorrect unemployment insurance payments in fiscal year 2022, excluding assessments for specific programs with higher vulnerability to risks, such as the Department of Labor’s Pandemic Unemployment Assistance benefits.

Critical government programs such as the 鈥 which will see $2 billion in funds allocated and includes the recent creation of the Office of Unemployment Insurance Modernization (OUIM) within the Department of Labor 鈥 offer some relief, but procuring and leveraging the funds is a heavy lift unto itself for any agency.

Moreover, 黑料大湿Posts Institute research shows that many government agencies want to spend more time in the fraud prevention stage of the process 鈥 which includes ID verification 鈥 but the actual time they are spending in this phase is less than desired. Suffice to say there is both a will and a way.

Increasing sophistication of bad actors

Bad actors are increasingly drawn to the UI system and are only adding to the challenges agencies currently face, leveraging sophisticated attacks through technology. Bot attacks, for example, are moving from generally easily detectable standard bots to periodic bots that very closely mimic the typing patterns of humans.

By harnessing the power of artificial intelligence (AI), bad actors are able to more easily scale their efforts while simultaneously creating more convincing attacks. Synthetic identities are one example of this. Creating synthetic IDs involves the use of a single element of legitimate personally identifiable information (most commonly a social security number) layered with fictitious elements. AI-fabricated details have the potential to add further legitimacy to the synthetic elements thereby complicating the overall detection of the identity in question.

Moving beyond individual synthetic IDs is the increasing trend toward fictitious businesses (also known as fictitious employers). Fraudsters can create fictitious companies, complete with fabricated records, websites, and even employees, to facilitate various types of fraud. These fictitious employers can be used to generate false employment histories, income verifications, and employment references.

This trend is on the rise and is made more difficult to detect given the structures of some agencies. With UI and tax commonly under separate leadership within government agencies, identifying fictitious employers is a significant challenge due to disparate data.

Finding the right balance

Combatting fraud, waste, and abuse must be considered in the context of ensuring good actors encounter a frictionless and simple path to receiving proper benefits. This is where the concept of step-up authentication can be leveraged to ensure that the amount of friction applied is commensurate with the level of threat perceived.

When data elements are verified, aligned, and consistent then of course less friction (if any) is the preferred path. Yet, in cases in which anomalies are detected or data is insufficiently verifiable, then a step-up path to further authenticate an identity may be needed. This process should seek to only put in the least amount of friction required to satisfy internal controls.

A variety of technology solutions are at the disposal of state agencies all with slightly different angles in the ID verification process.

Data analytics programs, for example, can detect patterns and anomalies in employment data, such as a high number of employees associated with a particular employer or unusual salary patterns. These advanced analytical techniques can be deployed into existing systems, thereby maintaining a frictionless customer experience while at the same time detecting fraudulent claims and facilitating smooth identity verification.

Applying deep behavioral and biometric analytics is another level of step-up authentication that can be particularly effective against synthetic IDs at the individual level. Indeed, biometric technologies such as facial recognition and behavioral analytics (for example, how someone holds a device) can be powerful antidotes to fraudulent activity; and matching real-time pictures (such as selfies) with government-issued identification documents can significantly deter fraudsters.

And when it comes to fictitious employers, coordinating across departments within an agency and comparing tax data with UI data is key. Identifying fictitious employers is greatly aided by visualizing and comparing trends across these two departments.


Although the U.S. Pandemic Unemployment Assistance benefits ceased on September 4th, 2021, individuals are still recovering from job losses due to the global pandemic, and there are concerns of . Consequently, a substantial number of unemployment insurance benefits are expected to be necessary to support those in need. Not to mention that 68% of government agencies have expressed concerns that they will continue to see more fraud than expected, according to the 黑料大湿Posts Institute鈥檚 2023 Government Fraud, Waste & Abuse Report.

These sentiments reflect their distress regarding the ongoing prevalence of fraudulent UI claims, which makes addressing the challenges faced by unemployment departments in verifying identities, detecting fraud, and preventing barriers to entry or workflow disruption so critical.

Successfully navigating these challenges will require government agencies to employ a balanced approach that combines industry expertise, technology, and collaboration.

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